modin.pandas.DataFrame.std

DataFrame.std(axis: Axis | None = None, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/base_overrides.py#L809-L829)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters:

  • axis ({index (0), columns (1)}) – For Series this parameter is unused and defaults to 0.

  • skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default False) – If True, Include only float, int, boolean columns. Not implemented for Series.

Return type:

Series

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

Examples

>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],
...                   'age': [21, 25, 62, 43],
...                   'height': [1.61, 1.87, 1.49, 2.01]}
...                  ).set_index('person_id')
>>> df    
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01
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The standard deviation of the columns can be found as follows:

>>> df.std()
age       18.786076
height     0.237417
dtype: float64
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Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.std(ddof=0)
age       16.269219
height     0.205609
dtype: float64
Copy
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